The present invention relates to a data processing technique that permits identification of reliable symbols in the presence of Inter-Symbol Interference (“ISI”) and other data correlated noise (collectively, “ISI”). Data correlated noise refers to a variety of phenomena in data processing systems in which a data signal interferes with itself at a destination. The present invention also relates to the use of reliable symbols to determine values of source symbols that are corrupted by ISI. The present invention finds application in systems where source symbols are members of high-order constellations. Previously, such systems have required the use of training symbols for operation in the presence of real-world ISI phenomenon.
FIG. 1 illustrates an exemplary data processing system 100 in which ISI may occur. A source 110 may generate a data signal X (herein, a “source data signal”). When delivered to a destination 120 as a received signal Y, the source data signal X may be corrupted by ISI sources 130. For example, multiple copies of a single data signal X may be captured at the destination 120, each copy being received with an unknown time shift and gain with respect to the other copies. Further, the time shifts and gains may vary over time.
ISI phenomena may be modeled mathematically. In the case where the data signal X is populated by a number of data symbols xn, captured signals yn at the destination 120 may be represented as:yn=a0·xn+f(xn−K1, . . . , xn−1, xn+1, . . . , xn+K2)+ωn.  (1)where a0 represents a gain factor associated with the channel 130, f(xn−K1, . . . , xn+K2) is a functional representation that relates the ISI to the symbols, xn−K1, . . . , xn+K2 causing ISI corruption and ωn represents corruption from other sources. In linear systems, Eq. 1 may reduce to:
                              y          n                =                              x            n                    +                                    ∑                                                i                  =                                      -                                          K                      1                                                                                        i                  ≠                  0                                                            K                2                                      ⁢                                          a                i                            ·                              x                                  n                  -                  i                                                              +                      ω            n                                              (        2        )            where a−K1, . . . aK2 represent the values of the impulse response of the channel. In accordance to common practice, the values ai have been normalized by the value of a0 in Eq. 2.
ISI may arise from a variety of real-world phenomena. Multipath is an example of ISI that occurs in wireless and other communication systems. In a wireless system 200, shown in FIG. 2, a base station 210 may transmit data addressed to a mobile station 220 over a region of space, typically a cell or a cell sector. The mobile station 220 may receive the signal via a direct line-of-sight path and also may receive copies of the data signal via other indirect paths. The indirect paths may be caused by reflections of the transmitted signal from structures in the transmission environment such as buildings, trucks, mountains and the like. At the mobile station 200, the directly received and indirectly received signals interfere with each other. The indirect transmissions, however, because they travel a longer propagation path before they reach the mobile station, are delayed with respect to the direct path signal.
ISI is seen as a serious impediment to the use of high-order constellations for data processing systems. A “constellation” represents a set of unique values that may be assigned to data symbols. Several examples are shown in FIG. 3. FIGS. 3(a)–(c) illustrate constellations for amplitude shift keying (“ASK”) applications where symbols can take one of four, eight or sixteen unique values. When compared to a binary symbol constellation, use of these constellations yields data throughput increases by factors of 2 (four levels), 3 (eight levels) or 4 (sixteen levels). FIGS. 3(d)–(f) illustrate constellations for quadrature amplitude modulation (“QAM”) applications where symbols can take one of four, sixteen or sixty-four unique values. When compared to a binary symbol constellation, use of these constellations yield data throughput increases of 2 (four levels), 4 (sixteen levels) and 6 (sixty-four levels). Thus, use of high-order constellations in data processing systems can yield increased throughput over binary systems within the same bandwidth.
The problem is that, when using high-order constellations, blind equalization (equalization without either an initial training sequence, or ‘refresher’ training sequences) is very hard to achieve because the detrimental effects of ISI increase with increasing constellation order.
There is a need in the art for a data transmission system that, in the presence of realistic levels of ISI, uses blind techniques to decode symbols from a high-order constellation.